title: A context-sensitive framework for the analysis of human signalling pathways in molecular interaction networks.
abstract: BACKGROUND
  A major challenge in systems biology is to reveal the cellular pathways that give rise to specific phenotypes and behaviours. Current techniques often rely on a network representation of molecular interactions, where each node represents a protein or a gene and each interaction is assigned a single static score. However, the use of single interaction scores fails to capture the tendency of proteins to favour different partners under distinct cellular conditions.
  
  
  RESULTS
  Here, we propose a novel context-sensitive network model, in which genes and protein nodes are assigned multiple contexts based on their gene ontology annotations, and their interactions are associated with multiple context-sensitive scores. Using this model, we developed a new approach and a corresponding tool, ContextNet, based on a dynamic programming algorithm for identifying signalling paths linking proteins to their downstream target genes. ContextNet finds high-ranking context-sensitive paths in the interactome, thereby revealing the intermediate proteins in the path and their path-specific contexts. We validated the model using 18 348 manually curated cellular paths derived from the SPIKE database. We next applied our framework to elucidate the responses of human primary lung cells to influenza infection. Top-ranking paths were much more likely to contain infection-related proteins, and this likelihood was highly correlated with path score. Moreover, the contexts assigned by the algorithm pointed to putative, as well as previously known responses to viral infection. Thus, context sensitivity is an important extension to current network biology models and can be efficiently used to elucidate cellular response mechanisms.
  
  
  BACKGROUND
  ContextNet is publicly available at http://netbio.bgu.ac.il/ContextNet.
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors.affiliation: Department of Computer Science, National Center for Biotechnology in the Negev, Ben-Gurion University of the Negev, Beer-Sheva 84105, Israel.
authors.forename: Alexander
authors.initials: A
authors.lastname: Lan
authors.forename: Michal
authors.initials: M
authors.lastname: Ziv-Ukelson
authors.forename: Esti
authors.initials: E
authors.lastname: Yeger-Lotem
mesh_terms: D000465:Algorithms; D063990:Gene Ontology; D053263:Gene Regulatory Networks; D006801:Humans; D008168:Lung / Q000378:metabolism / Q000821:virology; D008954:Models, Biological; D009975:Orthomyxoviridae / Q000378:metabolism; D025941:Protein Interaction Mapping / Q000379:methods*; D015398:Signal Transduction; D014764:Viral Proteins / Q000378:metabolism
chemical_list: C502893:PB2 protein, influenza virus; D014764:Viral Proteins